Survey Companion App Toolbox

Improving the quality of survey data on forcibly displaced populations

Let’s talk Quality!

Microdata quality in surveys on Forcibly Displaced Populations not only determines the reliability of currently collected information, but also the quality of any future information, such as remote sensing data, which requires validation through primary data.
Any errors introduced during the data collection process of the primary data, will most likely also create further issues with the information reliability in the future.
This can negatively influence program design, protection advocacy and policy making

The Companion App Survey Toolkit

Need to get tools for field staff with minimal programming skills, that can enhance the overall quality of survey data and support:

Sampling design
Questionnaire design
Data Collection Monitoring
Data Analysis

Mirroring WB “Survey Solution” project (JDC)…

all of which are critical in improving the quality of the data collected and managed on populations affected by forced displacement. With the scope of guaranteeing quality in the different stages of survey design and implementation, this project proposes different tools and methods for:

To cover the target populations in different population settings (i.e. returnee vs. IDPs, host- vs. camp-population) with the most effective sample design, the tool will allow implementation not only of standard sampling approaches (i.e. two stage stratified area sample), which can be used to sample households in host communities, as well as: Sampling from a spatial grid in the absence of any existing area- or list-frame; Use of gridded population data like WorldPop (https://www.worldpop.org/ );Sampling based on other satellite-based data.

Companion App design

Minimal scope - rather multiple simple companion apps to be used at different stages of the whole process than a single complex do-it-all one
Interface developed to help the diffusion of guidance: learning by doing to be paired with micro-learning for field staff in charge of the implementation on the ground
Tools built around free or open source solutions and designed to operate in capacity-constrained environments and to enable collaborative development.

Starting wih sampling frame

The construction of the sampling frame & the selection of the appropriate sampling design is crucial to produce statistically valid estimates that ensure results representativeness

Sampling frames are the backbones of probability-based sampling design. They plays one of the main roles in determining quality and reliability for the survey
Available frames often do not meet basic criteria: being current, comprehensive and sufficiently informative on the variable(s) of interest in the target population. In addition, target population size can be small and/or the population is hard to reach

Companion App {rmsSampling}

Scope:

Start from the country data as from ASR
Select the methodology based on a decision tree for each group
Define sample size based on expected accuracy

App Maturity: Alpha-version

Questionnaire design

Enabling users planning to collect data, with basic to minimal programming knowledge, to easily create questionnaires tailored for populations affected by forced displacement.

Questionnaire templates designed to incorporate recommended checks and verifications of question and response options.

How to implementing multiple interviewing modes during the survey as welll as multiple data collection waves.

Companion App {xlsFormUtil}

Scope:

Compare contextualized version with baseline global template
Select the methodology based on a decision tree for each group
Define sample size based on expected accuracy

App Maturity: Release-Candidate

Link to app

Companion App {surveyDesigner}

Scope:

Start questionnaire design from indicators selections
Provide scenarios over multiple data collection an waves and mode to optiomise the cost/quality threshold

App Maturity: alpha-version

Fieldwork and data quality assurance

Real time quality monitoring and the use of metadata and paradata facilitate quality control, but often presents challenges for the survey practitioners

Processing metadata and paradata opens avenues for understanding the behavior of the survey respondents, interviewers and improvement of the questionnaire.

Companion App {HighFrequencyChecks}

Scope:

Compare contextualized version with baseline global template
Select the methodology based on a decision tree for each group
Define sample size based on expected accuracy

App Maturity: beta-version

Link to app

Data Exploration and Indicator Calculation

Working in Excel for large complex survey dataset is not a sustainable option

Reproducibility is a key element to enforce

Companion App {koboCruncher}

Scope:

Ease relabeling, grouping , cross-tabulation
Direct connection to RIDL to enable data audit

App Maturity: beta-version

Link to app

Companion App {IndicatorCalc}

Scope:

Perform a mapping between a specific suvey and expected variables/modality for on survey
Ease variable recoding
Output standard report and dataset for indicators - included directly in RIDL

App Maturity: beta-version

Link to app

In-details Presentation of the 3 beta-version

Kobo Data Collection Management with {HighFrequencyChecks}
Kobo Survey Data Exploration with {KoboCruncher}
Apply Indicator calculations for RMS